Chapter 1.

Introduction

In which we try to explain why we consider artificial
intelligence to be a subject most worthy of study,
and in which we try to decide what exactly it is, this being a good thing to
decide before embarking.

Humankind has given itself the scientific
name homo sapiens--man the wise--because
our mental capacities are so important to our everyday lives and our
sense of self. The field of artificial
intelligence, or
AI, attempts to understand
intelligent entities. Thus, one reason to study it is to learn more
about ourselves. But unlike philosophy and
psychology, which are also concerned with
intelligence, AI strives to build intelligent entities as well
as understand them. Another reason to study AI is that these
constructed intelligent entities are interesting and useful in their
own right. AI has produced many significant and impressive products
even at this early stage in its development. Although no one can predict
the future in detail, it is clear that computers with human-level
intelligence (or better) would have a huge impact on our everyday
lives and on the future course of civilization.

AI addresses one of the ultimate puzzles. How is it possible for a slow, tiny
brain{brain}, whether biological or electronic, to perceive, understand, predict, and
manipulate a world far larger and more complicated than itself? How do we go
about making something with those properties? These are hard questions, but
unlike the search for faster-than-light travel or an antigravity device, the
researcher in AI has solid evidence that the quest is possible. All the
researcher has to do is look in the mirror to see an example of an intelligent
system.

AI is one of the newest disciplines. It was formally initiated in
1956, when the name was coined, although at that point work had been
under way for about five years. Along with modern genetics, it is
regularly cited as the ``field I would most like to be in'' by
scientists in other disciplines. A student in physics might
reasonably feel that all the good ideas have already been taken by
Galileo, Newton, Einstein, and the rest, and that it takes many years
of study before one can contribute new ideas. AI, on the other hand,
still has openings for a full-time Einstein.

The study of intelligence is also one of the oldest disciplines. For
over 2000 years, philosophers have tried to understand how seeing,
learning, remembering, and reasoning could, or should, be done. The
advent of usable computers in the early 1950s turned the learned but
armchair speculation concerning these mental faculties into a real
experimental and theoretical discipline. Many felt that the new
``Electronic Super-Brains'' had unlimited potential for
intelligence. ``Faster Than Einstein'' was a typical headline. But as
well as providing a vehicle for creating artificially intelligent
entities, the computer provides a tool for testing theories of
intelligence, and many theories failed to withstand the test--a case
of ``out of the armchair, into the fire.'' AI has turned out to be
more difficult than many at first imagined, and modern ideas are much
richer, more subtle, and more interesting as a result.

AI currently encompasses a huge variety of subfields, from general-purpose
areas such as perception and logical reasoning, to specific tasks such as
playing chess, proving mathematical theorems, writing poetry{poetry}, and diagnosing
diseases. Often, scientists in other fields move gradually into artificial
intelligence, where they find the tools and vocabulary to systematize and
automate the intellectual tasks on which they have been working all their
lives. Similarly, workers in AI can choose to apply their methods to any area
of human intellectual endeavor. In this sense, it is truly a universal field.

What is AI?

We have now explained why AI is exciting, but we have not said what it
is. We could just say, ``Well, it has to do with smart
programs, so let's get on and write some.'' But the history of science
shows that it is helpful to aim at the right goals. Early alchemists,
looking for a potion for eternal life and a method to turn lead into
gold, were probably off on the wrong foot. Only when the aim changed,
to that of finding explicit theories that gave accurate predictions of
the terrestrial world, in the same way that early astronomy predicted
the apparent motions of the stars and planets, could the scientific
method emerge and productive science take place.
Definitions of artificial intelligence according to eight recent
textbooks are shown in the table below. These definitions vary along two main
dimensions. The ones on top are concerned with thought
processes and reasoning, whereas the ones on the bottom
address behavior. Also, the definitions on the left measure
success in terms of human performance, whereas the ones on the
right measure against an ideal concept of intelligence, which
we will call rationality. A system is rational if it does the
right thing.

``The exciting new effort to make computers think ... machines
with minds, in the full and literal sense'' (Haugeland, 1985)

``The automation of activities that we associate with human thinking,
activities such as decision-making, problem solving, learning
...'' (Bellman, 1978)

``The study of mental faculties through the use of computational
models'' (Charniak and McDermott, 1985)

``The study of the computations that make it possible to perceive, reason,
and act'' (Winston, 1992)

``The art of creating machines that perform functions that require
intelligence when performed by people'' (Kurzweil, 1990)

``The study of how to make computers do things at which, at the moment, people are better'' (Rich and Knight, 1991)

``A field of study that seeks to explain and emulate intelligent behavior in
terms of computational processes'' (Schalkoff, 1990)

``The branch of computer science that is concerned with the automation
of intelligent behavior'' (Luger and Stubblefield, 1993)

This gives us four possible goals to pursue in artificial intelligence:

Systems that think like humans.

Systems that think rationally.

Systems that act like humans

Systems that act rationally

Historically, all four approaches have been followed. As one might expect, a
tension exists between approaches centered around humans and approaches
centered around rationality. (We should point out that by distinguishing
between human and rational behavior, we are not suggesting that
humans are necessarily ``irrational'' in the sense of ``emotionally unstable''
or ``insane.'' One merely need note that we often make mistakes; we are not
all chess grandmasters even though we may know all the rules of chess; and
unfortunately, not everyone gets an A on the exam. Some systematic errors in
human reasoning are cataloged by Kahneman et al..) A human-centered
approach must be an empirical science, involving hypothesis and experimental
confirmation. A rationalist approach involves a combination of mathematics
and engineering. People in each group sometimes cast aspersions on work done
in the other groups, but the truth is that each direction has yielded valuable
insights. Let us look at each in more detail.

Acting humanly: The Turing Test approach

The Turing Test, proposed by Alan Turing (Turing, 1950),
was designed to provide a satisfactory operational definition of intelligence.
Turing defined intelligent behavior as the ability to achieve human-level
performance in all cognitive tasks, sufficient to fool an interrogator.
Roughly speaking, the test he proposed is that the computer should be
interrogated by a human via a teletype, and passes the test if the
interrogator cannot tell if there is a computer or a human at the other end.
Chapter 26 discusses the details of the test, and whether or
not a computer is really intelligent if it passes. For now,
programming a computer
to pass the test provides plenty to work on. The computer would need to
possess the following capabilities:

natural language processing to enable it to
communicate successfully in English (or some other human language);

knowledge representation to store information provided
before or during the interrogation;

automated reasoning to use the stored information to answer
questions and to draw new conclusions;

machine learning to adapt to new circumstances and to
detect and extrapolate patterns.

Turing's test deliberately avoided direct physical interaction between
the interrogator and the computer, because physical simulation
of a person is unnecessary for intelligence. However, the so-called
total Turing Testincludes
a video signal so that the interrogator can test the subject's
perceptual abilities, as well as the opportunity for the interrogator to
pass physical objects ``through the hatch.'' To pass the total Turing Test,
the computer will need

computer vision to perceive objects, and

robotics to move them about.

Within AI, there has not been a big effort to try to pass the Turing
test. The issue of acting like a human comes up primarily when AI
programs have to interact with people, as when an expert system
explains how it came to its diagnosis, or a natural language
processing system has a dialogue with a user. These programs must
behave according to certain normal conventions of human interaction in
order to make themselves understood. The underlying representation
and reasoning in such a system may or may not be based on a human
model.

Thinking humanly: The cognitive modelling approach

If we are going to say that a given program thinks like a human, we must have
some way of determining how humans think. We need to get inside the
actual workings of human minds. There are two ways to do this: through
introspection--trying to catch our own thoughts as they go by--or through
psychological experiments. Once we have a sufficiently precise theory of the
mind, it becomes possible to express the theory as a computer program. If the
program's input/output and timing behavior matches human behavior, that is
evidence that some of the program's mechanisms may also be operating in
humans. For example, Newell and Simon, who developed GPS, the ``General
Problem Solver'' (Newell and Simon, 1961), were not content to have their program
correctly solve problems. They were more concerned with comparing the trace
of its reasoning steps to traces of human subjects solving the same problems.
This is in contrast to other researchers of the same time (such as Wang
(1960)), who were concerned with getting the right answers
regardless of how humans might do it. The interdisciplinary field of
cognitive science brings together computer models from AI and
experimental techniques from psychology to try to construct precise and
testable theories of the workings of the human mind.
Although cognitive science is a fascinating field in itself, we are not going
to be discussing it all that much in this book. We will occasionally comment
on similarities or differences between AI techniques and human cognition. Real
cognitive science, however, is necessarily based on experimental investigation
of actual humans or animals, and we assume that the reader only has access to
a computer for experimentation. We will simply note that AI and cognitive
science continue to fertilize each other, especially in the areas of vision,
natural language, and learning.
The history of psychological theories of cognition is
briefly covered on page 12.

Thinking rationally: The laws of thought approach

The Greek philosopher Aristotle was one of the first to attempt to codify
``right thinking,'' that is, irrefutable reasoning processes. His famous
syllogisms provided patterns for argument structures that always gave
correct conclusions given correct premises. For example, ``Socrates is a man;
all men are mortal; therefore Socrates is mortal.'' These laws of thought were
supposed to govern the operation of the mind, and initiated the field of
logic.

The development of formal logic in the late nineteenth and early twentieth
centuries, which we describe in more detail in
Chapter 6, provided a precise notation for statements
about all kinds of things in the world and the relations between them.
(Contrast this with ordinary arithmetic notation, which provides mainly for
equality and inequality statements about numbers.) By 1965, programs existed
that could, given enough time and memory, take a description of a problem in
logical notation and find the solution to the problem, if one exists. (If
there is no solution, the program might never stop looking for it.) The
so-called logicist tradition within artificial intelligence hopes to
build on such programs to create intelligent systems.

There are two main obstacles to this approach. First, it is not easy to take
informal knowledge and state it in the formal terms required by logical
notation, particularly when the knowledge is less than 100% certain. Second,
there is a big difference between being able to solve a problem ``in principle''
and doing so in practice. Even problems with just a few dozen facts can
exhaust the computational resources of any computer unless it has some
guidance as to which reasoning steps to try first. Although both of these
obstacles apply to any attempt to build computational reasoning systems,
they appeared first in the logicist tradition because the power of the
representation and reasoning systems are well-defined and fairly well
understood.

Acting rationally means acting so as to achieve one's goals, given
one's beliefs. An agent is just something that perceives and acts. (This
may be an unusual use of the word, but you will get used to it.) In this
approach, AI is viewed as the study and construction of rational agents.

In the ``laws of thought'' approach to AI, the whole emphasis was on correct
inferences. Making correct inferences is sometimes part of being a
rational agent, because one way to act rationally is to reason logically to
the conclusion that a given action will achieve one's goals, and then to act
on that conclusion. On the other hand, correct inference is not all of
rationality, because there are often situations where there is no provably
correct thing to do, yet something must still be done. There are also ways of
acting rationally that cannot be reasonably said to involve inference. For
example, pulling one's hand off of a hot stove is a reflex action that is more
successful than a slower action taken after careful deliberation.

All the ``cognitive skills'' needed for the Turing Test are there to allow
rational actions. Thus, we need the ability to represent knowledge and reason
with it because this enables us to reach good decisions in a wide variety of
situations. We need to be able to generate comprehensible sentences in natural
language because saying those sentences helps us get by in a complex society.
We need learning not just for erudition, but because having a better idea of
how the world works enables us to generate more effective strategies for
dealing with it. We need visual perception not just because seeing is fun, but
in order to get a better idea of what an action might achieve--for example,
being able to see a tasty morsel helps one to move toward it.

The study of AI as rational agent design therefore has two advantages. First,
it is more general than the ``laws of thought'' approach, because correct
inference is only a useful mechanism for achieving rationality, and not a
necessary one. Second, it is more amenable to scientific development than
approaches based on human behavior or human thought, because the standard of
rationality is clearly defined and completely general. Human behavior, on the
other hand, is well-adapted for one specific environment and is the product,
in part, of a complicated and largely unknown evolutionary process that still
may be far from achieving perfection.
This book will therefore concentrate on general principles of
rational agents, and on components for constructing them.
We will see that despite the apparent simplicity with which the problem can be
stated, an enormous variety of issues come up when we try to solve it.
Chapter 2 outlines some of these issues in more detail.
One important point to keep in mind: we will see before too long that
achieving perfect rationality--always doing the right thing--is not
possible in complicated environments. The computational demands are just too
high. However, for most of the book, we will adopt the working hypothesis that
understanding perfect decision making is a good place to start. It simplifies
the problem and provides the appropriate setting for
most of the foundational material in the field. Chapters
5 and 17 deal explicitly
with the issue of limited rationality--acting appropriately when
there is not enough time to do all the computations one might like.

The ``History of AI'' sections from the book are omitted from this online version.

The State of the Art

International grandmaster Arnold Denker studies the pieces on the board in
front of him. He realizes there is no hope; he must resign the game. His
opponent, Hitech, becomes the first computer program to defeat a
grandmaster in a game of chess.

``I want to go from Boston to San Francisco,'' the traveller says into the
microphone. ``What date will you be travelling on?'' is the reply. The
traveller explains she wants to go October 20th, nonstop, on the cheapest
available fare, returning on Sunday. A speech understanding program named
Pegasus handles the whole transaction, which results in a confirmed
reservation that saves the traveller $894 over the regular coach fare. Even
though the speech recognizer gets one out of ten words wrong, it is able to
recover from these errors because of its understanding of how dialogs are put
together.

An analyst in the Mission Operations room of the Jet Propulsion
Laboratory suddenly starts paying attention. A red message has
flashed onto the screen indicating an ``anomaly'' with the Voyager
spacecraft, which is somewhere in the vicinity of Neptune.
Fortunately, the analyst is able to correct the problem from the
ground. Operations personnel believe the problem might have been
overlooked had it not been for Marvel, a real-time expert
system that monitors the massive stream of data transmitted by the
spacecraft, handling routine tasks and alerting the analysts to more
serious problems.

Cruising the highway outside of Pittsburgh at a comfortable 55 mph, the man in
the driver's seat seems relaxed. He should be--for the past
90 miles, he has not had to touch the steering wheel.
The real driver is a robotic system that gathers input from video cameras,
sonar, and laser range finders attached to the van. It combines these inputs
with experience learned from training runs and succesfully computes
how to steer the vehicle.

A leading expert on lymph-node pathology describes a fiendishly
difficult case to the expert system, and examines the system's
diagnosis. He scoffs at the system's response. Only slightly worried,
the creators of the system suggest he ask the computer for an
explanation of the diagnosis. The machine points out the major factors
influencing its decision, and explains the subtle interaction of
several of the symptoms in this case. The expert admits his error,
eventually.

From a camera perched on a street light above the crossroads, the traffic
monitor watches the scene. If any humans were awake to read the main screen,
they would see ``Citroen 2CV turning from Place de la Concorde into Champs
Elysees,'' ``Large truck of unknown make stopped on Place de la Concorde,''
and so on into the night. And occasionally, ``Major incident on Place de la
Concorde, speeding van collided with motorcyclist,'' and an automatic
call to the emergency services.

These are just a few examples of artificial intelligence systems that exist
today. Not magic or science fiction--but rather science, engineering, and
mathematics, to which this book provides an introduction.

Summary

This chapter defines AI and establishes the cultural background against which
it has developed. Some of the important points are as follows:

Different people think of AI differently. Two important questions to
ask are: Are you concerned with thinking or behavior? Do you want to model
humans, or work from an ideal standard?

In this book, we adopt the view that intelligence is concerned mainly with rational action. Ideally, an
intelligent agent takes the best possible
action in a situation. We will study the problem of building agents
that are intelligent in this sense.

Philosophers (going back to 400 B.C.) made AI conceivable by considering
the ideas that the mind is in some ways like a machine, that it operates on
knowledge encoded in some internal language, and that thought can be used to
help arrive at the right actions to take.

Mathematicians provided the tools to manipulate statements of logical
certainty as well as uncertain, probabilistic statements. They also set the
groundwork for reasoning about algorithms.

Psychologists strengthened the idea that humans and other animals can be
considered information processing machines. Linguists showed that language
use fits into this model.

Computer engineering provided the artifact that makes AI applications
possible. AI programs tend to be large, and they could not work without the
great advances in speed and memory that the computer industry has provided.

The history of AI has had cycles of success, misplaced optimism, and
resulting cutbacks in enthusiasm and funding. There have also been cycles of
introducing new creative approaches and systematically refining the best ones.

Recent progress in understanding the theoretical basis for
intelligence has gone hand in hand with improvements in the capabilities of
real systems.